Denoising and Dimension Reduction in Feature Space

Speaker:	Professor Klaus-Robert Muller
		Technical University of Berlin
		and
		Fraunhofer Institut FIRST
		Intelligent Data Analysis Group (IDA)
		Kekulestr, Berlin

Title:		"Denoising and Dimension Reduction in Feature Space"


Date:		Monday, 2 June 2008

Time:		3:00pm - 4:00pm

Venue:		Lecture Theatre F
		(Leung Yat Sing Lecture Theatre, near lift 25/26)
		HKUST

Abstract:

The talk presents recent work that interestingly complements our
understanding of the VC picture in kernel based learning.

Our finding is that the relevant information of a supervised learning
problem is contained up to negligible error in a finite number of leading
kernel PCA components if the kernel matches the underlying learning
problem.  Thus, kernels not only transform data sets such that good
generalization can be achieved using only linear discriminant functions,
but this transformation is also performed in a manner which makes economic
use of feature space dimensions. In the best case, kernels provide
efficient implicit representations of the data for supervised learning
problems. Practically, we propose an algorithm which enables us to recover
the subspace and dimensionality relevant for good classification.  Our
algorithm can therefore be applied (1) to analyze the interplay of data
set and kernel in a geometric fashion, (2) to aid in model selection, and
to (3) denoise in feature space in order to yield better classification
results.

We complement our theoretical findings by reporting on applications of our
method to data from gene finding and brain computer interfacing.

This is joint work with Claudia Sanelli, Mikio Braun and Joachim M.
Buhmann.

******************
Biography:

Klaus-Robert Muller received the Diploma degree in mathematical physics in
1989 and the Ph.D. in theoretical computer science in 1992, both from
University of Karlsruhe, Germany. From 1992 to 1994 he worked as a
Postdoctoral fellow at GMD FIRST, in Berlin where he started to build up
the intelligent data analysis (IDA) group. From 1994 to 1995 he was a
European Community STP Research Fellow at University of Tokyo in Prof.
Amari's Lab. From 1995 on he is head of department of the IDA group at GMD
FIRST (since 2001 Fraunhofer FIRST) in Berlin and since 1999 he holds a
joint associate Professor position of GMD and University of Potsdam. In
2003 he became a full professor at University of Potsdam, in 2006 he
became chair of the machine learning department at TU Berlin. He has been
lecturing at Humboldt University, Technical University Berlin and
University of Potsdam. In 1999 he received the annual national prize for
pattern recognition (Olympus Prize) awarded by the German pattern
recognition society DAGM and in 2006 the SEL Alcatel communication award.
He serves in the editorial boards of Computational Statistics, IEEE
Transactions on Biomedical Engineering, Journal of Machine Learning
Research and in program and organization committees of various
international conferences.(services) His research areas include
statistical learning theory for neural networks, support vector machines
and ensemble learning techniques. He contributed to the field of signal
processing working on time-series analysis, statistical denoising methods
and blind source separation. His present application interests are
expanded to the analysis of biomedical data, most recently to brain
computer interfacing and genomic data analysis.